IA e Meta Mundo

Rio Innovation Week

I took part in Rio Innovation Week 2024 on the AI & Metaworld stage, in the panel “Demystifying Quantum Computing: Practical Applications and Possible Futures.” We discussed what quantum computing can do today, where it is realistically headed, and how to connect cutting-edge research to real-world problems.

TDC Business

The Developer Conference

At The Developer Conference (TDC Business), I presented “Adjust Your Programming Paradigm for a Quantum Mindset,” a talk aimed at software developers curious about quantum computing. I explored how quantum concepts such as superposition, measurement, and probabilistic outcomes require a fundamentally different way of thinking about algorithms and program structure. The session focused on building intuition rather than code, helping developers understand how quantum computing reshapes the mental models behind programming.

Tech Day

Open Co

Follow-up talk at Open Co Tech Day presenting the practical implementation of a quantum search algorithm, building on previous discussions of quantum computing fundamentals, and including a time computational complexity analysis.

Tech Day

Open Co

Invited talk at Open Co Tech Day introducing quantum computing concepts, recent developments, and practical entry points for industry engagement.

Heavy Ion Collision

This project aims to investigate relativistic heavy-ion collisions at the Large Hadron Collider using a model–to-data comparison approach. The work focuses on training statistical emulators to reproduce the output of computationally intensive theoretical models, avoiding the need to run full dynamical simulations. These emulators are then used to study correlations between experimental observables and physical parameters—such as shear and bulk viscosity, rapidity, transverse momentum spectra, among others—allowing an assessment of how different observables constrain the underlying collision dynamics.

Quantum Material Simulation

My research evolved through quantum simulations of molecular systems. I modeled electronic Hamiltonians within the Born–Oppenheimer approximation and expressed these Hamiltonians through second quantization, mapping them to qubit representations using the Jordan–Wigner transformation. I performed variational ground-state energy estimations in hybrid quantum–classical architectures, implementing the UCCSD ansatz with Qiskit Nature and conducting comparative experiments with PennyLane on simple molecular systems. Building on this variational framework, I explored neural-network quantum states—specifically restricted Boltzmann machines (RBMs)—as expressive ansätze for representing molecular ground states.

Quantum Computing

In this work, I studied quantum information encoding mechanisms, the construction of gate-based quantum circuits, and their implementation using Google Cirq, Amazon Braket, IBM Qiskit and CERN Qibo SDKs. I analyzed the execution of quantum operations and algorithms across different quantum hardware platforms—such as superconducting, trapped-ion, photonic, and neutral-atom systems—examining execution time, noise, and decoherence parameters. I implemented algorithms such as the Deutsch algorithm and Grover’s quantum search, and carried out introductory studies in quantum machine learning using quantum support vector machines (QSVM). To address noise in NISQ devices, I explored quantum error mitigation techniques, including zero-noise extrapolation (ZNE) and Clifford-based regression methods.